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Radiotherapy Planning, Computer-Assisted

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Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.

Journal of applied clinical medical physics
BACKGROUND: Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomo...

Deep learning-based automatic segmentation of cardiac substructures for lung cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation model...

Deep learning-based tools to distinguish plan-specific from generic deviations in EPID-based in vivo dosimetry.

Medical physics
BACKGROUND: Dose distributions calculated with electronic portal imaging device (EPID)-based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan-specific deviations. Generic deviations are characteristic to a class...

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy.

Physics in medicine and biology
Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the u...

Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: MRI-only planning relies on dosimetrically accurate synthetic-CT (sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of sCTs generated using a deep learning algorithm for pelvic, brain and hea...

ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer - study protocol.

BMJ open
INTRODUCTION: Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%-40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has bee...

Clinical evaluation of deep learning and atlas-based auto-segmentation for organs at risk delineation.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Manual delineation of organs at risk and clinical target volumes is essential in radiotherapy planning. Atlas-based auto-segmentation (ABAS) algorithms have become available and been shown to provide accurate contouring for various anatomical sites. ...

Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy.

International journal of radiation oncology, biology, physics
PURPOSE: Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractions is sometimes considered controversial, as the nominal biologically effective dose (BED) of 100 Gy is felt by some to be insufficient for long-term ...

Deep learning based MLC aperture and monitor unit prediction as a warm start for breast VMAT optimisation.

Physics in medicine and biology
. Automated treatment planning today is focussed on non-exact, two-step procedures. Firstly, dose-volume histograms (DVHs) or 3D dose distributions are predicted from the patient anatomy. Secondly, these are converted in multi-leaf collimator (MLC) a...

Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study.

Breast (Edinburgh, Scotland)
PURPOSE: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potentia...